Overview

Dataset statistics

Number of variables21
Number of observations42307
Missing cells1771
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 MiB
Average record size in memory116.1 B

Variable types

Numeric8
Categorical13

Alerts

DisbursementDate has a high cardinality: 916 distinct valuesHigh cardinality
ApprovalDate has a high cardinality: 3868 distinct valuesHigh cardinality
City has a high cardinality: 2703 distinct valuesHigh cardinality
State has a high cardinality: 51 distinct valuesHigh cardinality
BankState has a high cardinality: 51 distinct valuesHigh cardinality
DisbursementGross has a high cardinality: 2694 distinct valuesHigh cardinality
GrAppv has a high cardinality: 1425 distinct valuesHigh cardinality
SBA_Appv has a high cardinality: 2005 distinct valuesHigh cardinality
BankState is highly overall correlated with StateHigh correlation
State is highly overall correlated with BankStateHigh correlation
LowDoc is highly imbalanced (63.8%)Imbalance
MIS_Status is highly imbalanced (50.8%)Imbalance
RevLineCr has 1079 (2.6%) missing valuesMissing
LowDoc has 531 (1.3%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
NoEmp has 2994 (7.1%) zerosZeros
CreateJob has 28889 (68.3%) zerosZeros
RetainedJob has 26056 (61.6%) zerosZeros
FranchiseCode has 26392 (62.4%) zerosZeros
Sector has 9798 (23.2%) zerosZeros

Reproduction

Analysis started2024-01-23 03:29:25.004749
Analysis finished2024-01-23 03:29:30.243696
Duration5.24 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct42307
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21153
Minimum0
Maximum42306
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size330.7 KiB
2024-01-23T12:29:30.283046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2115.3
Q110576.5
median21153
Q331729.5
95-th percentile40190.7
Maximum42306
Range42306
Interquartile range (IQR)21153

Descriptive statistics

Standard deviation12213.123
Coefficient of variation (CV)0.57737074
Kurtosis-1.2
Mean21153
Median Absolute Deviation (MAD)10577
Skewness0
Sum8.9491997 × 108
Variance1.4916038 × 108
MonotonicityStrictly increasing
2024-01-23T12:29:30.339243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
28199 1
 
< 0.1%
28201 1
 
< 0.1%
28202 1
 
< 0.1%
28203 1
 
< 0.1%
28204 1
 
< 0.1%
28205 1
 
< 0.1%
28206 1
 
< 0.1%
28207 1
 
< 0.1%
28208 1
 
< 0.1%
Other values (42297) 42297
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
42306 1
< 0.1%
42305 1
< 0.1%
42304 1
< 0.1%
42303 1
< 0.1%
42302 1
< 0.1%
42301 1
< 0.1%
42300 1
< 0.1%
42299 1
< 0.1%
42298 1
< 0.1%
42297 1
< 0.1%

Term
Real number (ℝ)

Distinct228
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.60167
Minimum0
Maximum360
Zeros94
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size330.7 KiB
2024-01-23T12:29:30.389449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q156
median82
Q3168
95-th percentile293
Maximum360
Range360
Interquartile range (IQR)112

Descriptive statistics

Standard deviation84.569847
Coefficient of variation (CV)0.77871587
Kurtosis-0.28186028
Mean108.60167
Median Absolute Deviation (MAD)30
Skewness1.0246757
Sum4594611
Variance7152.0589
MonotonicityNot monotonic
2024-01-23T12:29:30.444148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 3649
 
8.6%
83 2863
 
6.8%
84 1546
 
3.7%
57 1325
 
3.1%
81 1300
 
3.1%
58 1295
 
3.1%
59 1161
 
2.7%
56 1046
 
2.5%
240 892
 
2.1%
241 875
 
2.1%
Other values (218) 26355
62.3%
ValueCountFrequency (%)
0 94
0.2%
1 54
 
0.1%
2 75
 
0.2%
3 62
 
0.1%
4 91
 
0.2%
5 140
0.3%
6 145
0.3%
7 158
0.4%
8 188
0.4%
9 232
0.5%
ValueCountFrequency (%)
360 1
 
< 0.1%
325 7
 
< 0.1%
312 11
 
< 0.1%
311 22
 
0.1%
310 19
 
< 0.1%
309 45
 
0.1%
308 69
0.2%
306 72
0.2%
303 122
0.3%
302 130
0.3%

NoEmp
Real number (ℝ)

ZEROS 

Distinct196
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7043043
Minimum0
Maximum202
Zeros2994
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size330.7 KiB
2024-01-23T12:29:30.497645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q312
95-th percentile40
Maximum202
Range202
Interquartile range (IQR)10

Descriptive statistics

Standard deviation17.488022
Coefficient of variation (CV)1.8020892
Kurtosis40.742751
Mean9.7043043
Median Absolute Deviation (MAD)3
Skewness5.4942159
Sum410560
Variance305.83092
MonotonicityNot monotonic
2024-01-23T12:29:30.551493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5563
13.1%
2 5518
13.0%
4 4902
11.6%
1 4433
 
10.5%
5 3146
 
7.4%
0 2994
 
7.1%
6 1691
 
4.0%
15 975
 
2.3%
16 914
 
2.2%
7 901
 
2.1%
Other values (186) 11270
26.6%
ValueCountFrequency (%)
0 2994
7.1%
1 4433
10.5%
2 5518
13.0%
3 5563
13.1%
4 4902
11.6%
5 3146
7.4%
6 1691
 
4.0%
7 901
 
2.1%
8 602
 
1.4%
9 544
 
1.3%
ValueCountFrequency (%)
202 1
 
< 0.1%
198 1
 
< 0.1%
197 2
< 0.1%
195 1
 
< 0.1%
194 2
< 0.1%
193 2
< 0.1%
192 2
< 0.1%
191 3
< 0.1%
189 3
< 0.1%
188 2
< 0.1%

NewExist
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size330.7 KiB
1.0
33405 
2.0
8902 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters126921
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 33405
79.0%
2.0 8902
 
21.0%

Length

2024-01-23T12:29:30.600493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-23T12:29:30.641202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 33405
79.0%
2.0 8902
 
21.0%

Most occurring characters

ValueCountFrequency (%)
. 42307
33.3%
0 42307
33.3%
1 33405
26.3%
2 8902
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84614
66.7%
Other Punctuation 42307
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42307
50.0%
1 33405
39.5%
2 8902
 
10.5%
Other Punctuation
ValueCountFrequency (%)
. 42307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 126921
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 42307
33.3%
0 42307
33.3%
1 33405
26.3%
2 8902
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 42307
33.3%
0 42307
33.3%
1 33405
26.3%
2 8902
 
7.0%

CreateJob
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1837285
Minimum0
Maximum70
Zeros28889
Zeros (%)68.3%
Negative0
Negative (%)0.0%
Memory size330.7 KiB
2024-01-23T12:29:30.684971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile13
Maximum70
Range70
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.0939801
Coefficient of variation (CV)2.3326985
Kurtosis23.628207
Mean2.1837285
Median Absolute Deviation (MAD)0
Skewness4.0134768
Sum92387
Variance25.948633
MonotonicityNot monotonic
2024-01-23T12:29:30.738802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 28889
68.3%
3 3293
 
7.8%
1 2519
 
6.0%
4 1334
 
3.2%
8 665
 
1.6%
9 579
 
1.4%
2 548
 
1.3%
10 511
 
1.2%
7 484
 
1.1%
11 448
 
1.1%
Other values (39) 3037
 
7.2%
ValueCountFrequency (%)
0 28889
68.3%
1 2519
 
6.0%
2 548
 
1.3%
3 3293
 
7.8%
4 1334
 
3.2%
5 73
 
0.2%
6 250
 
0.6%
7 484
 
1.1%
8 665
 
1.6%
9 579
 
1.4%
ValueCountFrequency (%)
70 1
 
< 0.1%
60 3
 
< 0.1%
57 5
 
< 0.1%
56 5
 
< 0.1%
50 6
 
< 0.1%
48 12
< 0.1%
47 15
< 0.1%
46 19
< 0.1%
45 16
< 0.1%
40 17
< 0.1%

RetainedJob
Real number (ℝ)

ZEROS 

Distinct83
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4781478
Minimum0
Maximum140
Zeros26056
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size330.7 KiB
2024-01-23T12:29:30.789264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile15
Maximum140
Range140
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.1136484
Coefficient of variation (CV)2.3327497
Kurtosis40.299963
Mean3.4781478
Median Absolute Deviation (MAD)0
Skewness5.1823038
Sum147150
Variance65.83129
MonotonicityNot monotonic
2024-01-23T12:29:30.842613image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26056
61.6%
1 3846
 
9.1%
8 1227
 
2.9%
9 1060
 
2.5%
3 1057
 
2.5%
7 986
 
2.3%
10 837
 
2.0%
2 803
 
1.9%
11 795
 
1.9%
12 769
 
1.8%
Other values (73) 4871
 
11.5%
ValueCountFrequency (%)
0 26056
61.6%
1 3846
 
9.1%
2 803
 
1.9%
3 1057
 
2.5%
4 687
 
1.6%
5 329
 
0.8%
6 575
 
1.4%
7 986
 
2.3%
8 1227
 
2.9%
9 1060
 
2.5%
ValueCountFrequency (%)
140 1
 
< 0.1%
136 1
 
< 0.1%
130 1
 
< 0.1%
118 1
 
< 0.1%
102 2
 
< 0.1%
100 4
< 0.1%
95 4
< 0.1%
91 7
< 0.1%
90 6
< 0.1%
87 7
< 0.1%

FranchiseCode
Real number (ℝ)

ZEROS 

Distinct271
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1955.056
Minimum0
Maximum90709
Zeros26392
Zeros (%)62.4%
Negative0
Negative (%)0.0%
Memory size330.7 KiB
2024-01-23T12:29:30.985308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum90709
Range90709
Interquartile range (IQR)1

Descriptive statistics

Standard deviation10541.389
Coefficient of variation (CV)5.3918602
Kurtosis35.690306
Mean1955.056
Median Absolute Deviation (MAD)0
Skewness5.919311
Sum82712555
Variance1.1112088 × 108
MonotonicityNot monotonic
2024-01-23T12:29:31.034743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26392
62.4%
1 14033
33.2%
960 182
 
0.4%
27760 21
 
< 0.1%
74750 20
 
< 0.1%
72590 18
 
< 0.1%
73000 18
 
< 0.1%
73675 15
 
< 0.1%
34850 15
 
< 0.1%
36680 14
 
< 0.1%
Other values (261) 1579
 
3.7%
ValueCountFrequency (%)
0 26392
62.4%
1 14033
33.2%
960 182
 
0.4%
5725 1
 
< 0.1%
6410 1
 
< 0.1%
9120 1
 
< 0.1%
9450 1
 
< 0.1%
10482 1
 
< 0.1%
10494 1
 
< 0.1%
10528 3
 
< 0.1%
ValueCountFrequency (%)
90709 1
 
< 0.1%
89769 2
< 0.1%
89655 1
 
< 0.1%
89640 2
< 0.1%
89352 1
 
< 0.1%
89350 1
 
< 0.1%
88875 2
< 0.1%
88355 1
 
< 0.1%
87350 3
< 0.1%
86720 3
< 0.1%

RevLineCr
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing1079
Missing (%)2.6%
Memory size41.6 KiB
N
27618 
Y
7353 
0
5561 
T
 
696

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41228
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd row0
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 27618
65.3%
Y 7353
 
17.4%
0 5561
 
13.1%
T 696
 
1.6%
(Missing) 1079
 
2.6%

Length

2024-01-23T12:29:31.080140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-23T12:29:31.119014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
n 27618
67.0%
y 7353
 
17.8%
0 5561
 
13.5%
t 696
 
1.7%

Most occurring characters

ValueCountFrequency (%)
N 27618
67.0%
Y 7353
 
17.8%
0 5561
 
13.5%
T 696
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 35667
86.5%
Decimal Number 5561
 
13.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 27618
77.4%
Y 7353
 
20.6%
T 696
 
2.0%
Decimal Number
ValueCountFrequency (%)
0 5561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35667
86.5%
Common 5561
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 27618
77.4%
Y 7353
 
20.6%
T 696
 
2.0%
Common
ValueCountFrequency (%)
0 5561
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 27618
67.0%
Y 7353
 
17.8%
0 5561
 
13.5%
T 696
 
1.7%

LowDoc
Categorical

IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing531
Missing (%)1.3%
Memory size41.7 KiB
N
34313 
Y
5277 
0
 
684
A
 
570
S
 
540

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41776
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 34313
81.1%
Y 5277
 
12.5%
0 684
 
1.6%
A 570
 
1.3%
S 540
 
1.3%
C 392
 
0.9%
(Missing) 531
 
1.3%

Length

2024-01-23T12:29:31.161960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-23T12:29:31.202088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
n 34313
82.1%
y 5277
 
12.6%
0 684
 
1.6%
a 570
 
1.4%
s 540
 
1.3%
c 392
 
0.9%

Most occurring characters

ValueCountFrequency (%)
N 34313
82.1%
Y 5277
 
12.6%
0 684
 
1.6%
A 570
 
1.4%
S 540
 
1.3%
C 392
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 41092
98.4%
Decimal Number 684
 
1.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 34313
83.5%
Y 5277
 
12.8%
A 570
 
1.4%
S 540
 
1.3%
C 392
 
1.0%
Decimal Number
ValueCountFrequency (%)
0 684
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41092
98.4%
Common 684
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 34313
83.5%
Y 5277
 
12.8%
A 570
 
1.4%
S 540
 
1.3%
C 392
 
1.0%
Common
ValueCountFrequency (%)
0 684
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 34313
82.1%
Y 5277
 
12.6%
0 684
 
1.6%
A 570
 
1.4%
S 540
 
1.3%
C 392
 
0.9%

DisbursementDate
Categorical

HIGH CARDINALITY 

Distinct916
Distinct (%)2.2%
Missing150
Missing (%)0.4%
Memory size122.2 KiB
30-Nov-03
 
1634
31-Jul-95
 
1480
31-Dec-05
 
1076
31-Jan-98
 
878
31-Jan-05
 
871
Other values (911)
36218 

Length

Max length9
Median length9
Mean length8.93695
Min length8

Characters and Unicode

Total characters376755
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row31-Jan-98
2nd row31-Oct-93
3rd row31-Aug-01
4th row31-Aug-07
5th row8-Jun-83

Common Values

ValueCountFrequency (%)
30-Nov-03 1634
 
3.9%
31-Jul-95 1480
 
3.5%
31-Dec-05 1076
 
2.5%
31-Jan-98 878
 
2.1%
31-Jan-05 871
 
2.1%
30-Nov-04 830
 
2.0%
28-Feb-05 694
 
1.6%
30-Apr-95 689
 
1.6%
31-Aug-07 675
 
1.6%
30-Apr-00 540
 
1.3%
Other values (906) 32790
77.5%

Length

2024-01-23T12:29:31.247459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
30-nov-03 1634
 
3.9%
31-jul-95 1480
 
3.5%
31-dec-05 1076
 
2.6%
31-jan-98 878
 
2.1%
31-jan-05 871
 
2.1%
30-nov-04 830
 
2.0%
28-feb-05 694
 
1.6%
30-apr-95 689
 
1.6%
31-aug-07 675
 
1.6%
30-apr-00 540
 
1.3%
Other values (906) 32790
77.8%

Most occurring characters

ValueCountFrequency (%)
- 84314
22.4%
0 39549
 
10.5%
3 36554
 
9.7%
1 31284
 
8.3%
9 19493
 
5.2%
J 12359
 
3.3%
u 10612
 
2.8%
5 9206
 
2.4%
e 9024
 
2.4%
a 8906
 
2.4%
Other values (23) 115454
30.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 165970
44.1%
Dash Punctuation 84314
22.4%
Lowercase Letter 84314
22.4%
Uppercase Letter 42157
 
11.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 10612
12.6%
e 9024
10.7%
a 8906
10.6%
p 7821
9.3%
n 7786
9.2%
c 7217
8.6%
r 6879
8.2%
v 4691
 
5.6%
o 4691
 
5.6%
l 4573
 
5.4%
Other values (4) 12114
14.4%
Decimal Number
ValueCountFrequency (%)
0 39549
23.8%
3 36554
22.0%
1 31284
18.8%
9 19493
11.7%
5 9206
 
5.5%
8 7673
 
4.6%
2 7068
 
4.3%
7 5596
 
3.4%
6 4992
 
3.0%
4 4555
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
J 12359
29.3%
A 7894
18.7%
N 4691
 
11.1%
M 4224
 
10.0%
O 3965
 
9.4%
D 3252
 
7.7%
F 2910
 
6.9%
S 2862
 
6.8%
Dash Punctuation
ValueCountFrequency (%)
- 84314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 250284
66.4%
Latin 126471
33.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 12359
 
9.8%
u 10612
 
8.4%
e 9024
 
7.1%
a 8906
 
7.0%
A 7894
 
6.2%
p 7821
 
6.2%
n 7786
 
6.2%
c 7217
 
5.7%
r 6879
 
5.4%
N 4691
 
3.7%
Other values (12) 43282
34.2%
Common
ValueCountFrequency (%)
- 84314
33.7%
0 39549
15.8%
3 36554
14.6%
1 31284
 
12.5%
9 19493
 
7.8%
5 9206
 
3.7%
8 7673
 
3.1%
2 7068
 
2.8%
7 5596
 
2.2%
6 4992
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 84314
22.4%
0 39549
 
10.5%
3 36554
 
9.7%
1 31284
 
8.3%
9 19493
 
5.2%
J 12359
 
3.3%
u 10612
 
2.8%
5 9206
 
2.4%
e 9024
 
2.4%
a 8906
 
2.4%
Other values (23) 115454
30.6%

MIS_Status
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size330.7 KiB
1
37767 
0
4540 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42307
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Length

2024-01-23T12:29:31.289109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-23T12:29:31.334695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Most occurring characters

ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42307
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Common 42307
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Sector
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.933439
Minimum0
Maximum81
Zeros9798
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size330.7 KiB
2024-01-23T12:29:31.377821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122
median33
Q344
95-th percentile72
Maximum81
Range81
Interquartile range (IQR)22

Descriptive statistics

Standard deviation22.291386
Coefficient of variation (CV)0.67686178
Kurtosis-0.89972227
Mean32.933439
Median Absolute Deviation (MAD)11
Skewness-0.11631806
Sum1393315
Variance496.90589
MonotonicityNot monotonic
2024-01-23T12:29:31.425482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 9798
23.2%
42 7337
17.3%
33 5050
11.9%
44 3868
 
9.1%
23 3867
 
9.1%
61 2505
 
5.9%
72 2478
 
5.9%
22 1998
 
4.7%
62 1191
 
2.8%
53 896
 
2.1%
Other values (14) 3319
 
7.8%
ValueCountFrequency (%)
0 9798
23.2%
11 7
 
< 0.1%
21 28
 
0.1%
22 1998
 
4.7%
23 3867
 
9.1%
31 138
 
0.3%
32 865
 
2.0%
33 5050
11.9%
42 7337
17.3%
44 3868
 
9.1%
ValueCountFrequency (%)
81 169
 
0.4%
72 2478
5.9%
71 337
 
0.8%
62 1191
2.8%
61 2505
5.9%
56 672
 
1.6%
55 29
 
0.1%
54 267
 
0.6%
53 896
 
2.1%
52 84
 
0.2%

ApprovalDate
Categorical

HIGH CARDINALITY 

Distinct3868
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size242.0 KiB
7-Oct-03
 
1347
10-May-07
 
661
26-Aug-04
 
620
26-Oct-99
 
595
13-Nov-08
 
422
Other values (3863)
38662 

Length

Max length9
Median length9
Mean length8.7187936
Min length8

Characters and Unicode

Total characters368866
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique726 ?
Unique (%)1.7%

Sample

1st row22-Sep-06
2nd row30-Jun-92
3rd row18-Apr-01
4th row6-Oct-03
5th row17-Dec-99

Common Values

ValueCountFrequency (%)
7-Oct-03 1347
 
3.2%
10-May-07 661
 
1.6%
26-Aug-04 620
 
1.5%
26-Oct-99 595
 
1.4%
13-Nov-08 422
 
1.0%
26-Jan-07 359
 
0.8%
14-Mar-03 330
 
0.8%
22-Feb-08 324
 
0.8%
22-Aug-00 295
 
0.7%
15-Feb-95 263
 
0.6%
Other values (3858) 37091
87.7%

Length

2024-01-23T12:29:31.473846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7-oct-03 1347
 
3.2%
10-may-07 661
 
1.6%
26-aug-04 620
 
1.5%
26-oct-99 595
 
1.4%
13-nov-08 422
 
1.0%
26-jan-07 359
 
0.8%
14-mar-03 330
 
0.8%
22-feb-08 324
 
0.8%
22-aug-00 295
 
0.7%
15-feb-95 263
 
0.6%
Other values (3858) 37091
87.7%

Most occurring characters

ValueCountFrequency (%)
- 84614
22.9%
0 33007
 
8.9%
1 23038
 
6.2%
2 21109
 
5.7%
9 20658
 
5.6%
3 11777
 
3.2%
7 10586
 
2.9%
a 10471
 
2.8%
6 10289
 
2.8%
u 10052
 
2.7%
Other values (23) 133265
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 157331
42.7%
Dash Punctuation 84614
22.9%
Lowercase Letter 84614
22.9%
Uppercase Letter 42307
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10471
12.4%
u 10052
11.9%
e 9917
11.7%
c 8022
9.5%
r 7006
8.3%
p 6724
7.9%
n 6437
7.6%
t 4751
 
5.6%
g 3935
 
4.7%
v 3908
 
4.6%
Other values (4) 13391
15.8%
Decimal Number
ValueCountFrequency (%)
0 33007
21.0%
1 23038
14.6%
2 21109
13.4%
9 20658
13.1%
3 11777
 
7.5%
7 10586
 
6.7%
6 10289
 
6.5%
8 9129
 
5.8%
4 9085
 
5.8%
5 8653
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
J 9443
22.3%
M 7145
16.9%
A 7143
16.9%
O 4751
11.2%
N 3908
9.2%
S 3516
 
8.3%
D 3271
 
7.7%
F 3130
 
7.4%
Dash Punctuation
ValueCountFrequency (%)
- 84614
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 241945
65.6%
Latin 126921
34.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10471
 
8.3%
u 10052
 
7.9%
e 9917
 
7.8%
J 9443
 
7.4%
c 8022
 
6.3%
M 7145
 
5.6%
A 7143
 
5.6%
r 7006
 
5.5%
p 6724
 
5.3%
n 6437
 
5.1%
Other values (12) 44561
35.1%
Common
ValueCountFrequency (%)
- 84614
35.0%
0 33007
 
13.6%
1 23038
 
9.5%
2 21109
 
8.7%
9 20658
 
8.5%
3 11777
 
4.9%
7 10586
 
4.4%
6 10289
 
4.3%
8 9129
 
3.8%
4 9085
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 84614
22.9%
0 33007
 
8.9%
1 23038
 
6.2%
2 21109
 
5.7%
9 20658
 
5.6%
3 11777
 
3.2%
7 10586
 
2.9%
a 10471
 
2.8%
6 10289
 
2.8%
u 10052
 
2.7%
Other values (23) 133265
36.1%

ApprovalFY
Real number (ℝ)

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.5378
Minimum1974
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.7 KiB
2024-01-23T12:29:31.519216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1974
5-th percentile1991
Q11997
median2003
Q32006
95-th percentile2010
Maximum2014
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.8605268
Coefficient of variation (CV)0.0029280121
Kurtosis0.16185187
Mean2001.5378
Median Absolute Deviation (MAD)4
Skewness-0.69419236
Sum84679059
Variance34.345775
MonotonicityNot monotonic
2024-01-23T12:29:31.570626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2004 4708
 
11.1%
2007 3777
 
8.9%
2006 3314
 
7.8%
2003 3293
 
7.8%
2005 2712
 
6.4%
1995 2552
 
6.0%
2000 2295
 
5.4%
1996 1739
 
4.1%
2002 1726
 
4.1%
2008 1723
 
4.1%
Other values (28) 14468
34.2%
ValueCountFrequency (%)
1974 4
 
< 0.1%
1977 5
 
< 0.1%
1979 21
 
< 0.1%
1980 56
0.1%
1981 13
 
< 0.1%
1982 78
0.2%
1983 80
0.2%
1984 79
0.2%
1985 134
0.3%
1986 77
0.2%
ValueCountFrequency (%)
2014 9
 
< 0.1%
2013 105
 
0.2%
2012 318
 
0.8%
2011 781
 
1.8%
2010 948
 
2.2%
2009 1158
 
2.7%
2008 1723
4.1%
2007 3777
8.9%
2006 3314
7.8%
2005 2712
6.4%

City
Categorical

HIGH CARDINALITY 

Distinct2703
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size168.4 KiB
HOUSTON
 
1220
PITTSBURGH
 
955
SALT LAKE CITY
 
656
PHILADELPHIA
 
612
NASHVILLE
 
599
Other values (2698)
38265 

Length

Max length30
Median length27
Mean length8.8926892
Min length3

Characters and Unicode

Total characters376223
Distinct characters65
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique827 ?
Unique (%)2.0%

Sample

1st rowPHOENIX
2nd rowMCALESTER
3rd rowHAWTHORNE
4th rowNASHVILLE
5th rowPOMONA

Common Values

ValueCountFrequency (%)
HOUSTON 1220
 
2.9%
PITTSBURGH 955
 
2.3%
SALT LAKE CITY 656
 
1.6%
PHILADELPHIA 612
 
1.4%
NASHVILLE 599
 
1.4%
POMONA 584
 
1.4%
CLARENCE 535
 
1.3%
NEW YORK 475
 
1.1%
ORLANDO 470
 
1.1%
SAN DIEGO 444
 
1.0%
Other values (2693) 35757
84.5%

Length

2024-01-23T12:29:31.624830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city 1492
 
2.7%
san 1325
 
2.4%
houston 1224
 
2.3%
pittsburgh 958
 
1.8%
lake 812
 
1.5%
salt 716
 
1.3%
new 616
 
1.1%
philadelphia 614
 
1.1%
nashville 600
 
1.1%
pomona 584
 
1.1%
Other values (2347) 45399
83.5%

Most occurring characters

ValueCountFrequency (%)
A 36799
 
9.8%
E 32773
 
8.7%
O 29514
 
7.8%
L 29423
 
7.8%
N 28734
 
7.6%
I 24008
 
6.4%
S 23939
 
6.4%
R 22162
 
5.9%
T 20421
 
5.4%
C 13141
 
3.5%
Other values (55) 115309
30.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 353936
94.1%
Space Separator 12034
 
3.2%
Lowercase Letter 9453
 
2.5%
Other Punctuation 370
 
0.1%
Open Punctuation 262
 
0.1%
Close Punctuation 151
 
< 0.1%
Decimal Number 10
 
< 0.1%
Dash Punctuation 6
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 36799
 
10.4%
E 32773
 
9.3%
O 29514
 
8.3%
L 29423
 
8.3%
N 28734
 
8.1%
I 24008
 
6.8%
S 23939
 
6.8%
R 22162
 
6.3%
T 20421
 
5.8%
C 13141
 
3.7%
Other values (16) 93022
26.3%
Lowercase Letter
ValueCountFrequency (%)
o 1044
11.0%
e 998
10.6%
a 940
9.9%
n 940
9.9%
l 873
9.2%
r 813
8.6%
i 696
 
7.4%
s 567
 
6.0%
t 499
 
5.3%
u 258
 
2.7%
Other values (16) 1825
19.3%
Decimal Number
ValueCountFrequency (%)
6 2
20.0%
8 2
20.0%
5 2
20.0%
0 2
20.0%
2 2
20.0%
Other Punctuation
ValueCountFrequency (%)
. 271
73.2%
' 59
 
15.9%
, 40
 
10.8%
Space Separator
ValueCountFrequency (%)
12034
100.0%
Open Punctuation
ValueCountFrequency (%)
( 262
100.0%
Close Punctuation
ValueCountFrequency (%)
) 151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 363389
96.6%
Common 12834
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 36799
 
10.1%
E 32773
 
9.0%
O 29514
 
8.1%
L 29423
 
8.1%
N 28734
 
7.9%
I 24008
 
6.6%
S 23939
 
6.6%
R 22162
 
6.1%
T 20421
 
5.6%
C 13141
 
3.6%
Other values (42) 102475
28.2%
Common
ValueCountFrequency (%)
12034
93.8%
. 271
 
2.1%
( 262
 
2.0%
) 151
 
1.2%
' 59
 
0.5%
, 40
 
0.3%
- 6
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
5 2
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 36799
 
9.8%
E 32773
 
8.7%
O 29514
 
7.8%
L 29423
 
7.8%
N 28734
 
7.6%
I 24008
 
6.4%
S 23939
 
6.4%
R 22162
 
5.9%
T 20421
 
5.4%
C 13141
 
3.5%
Other values (55) 115309
30.6%

State
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
CA
6893 
TX
4095 
NY
2953 
PA
2849 
FL
 
1920
Other values (46)
23597 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters84614
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowOK
3rd rowNJ
4th rowTN
5th rowCA

Common Values

ValueCountFrequency (%)
CA 6893
 
16.3%
TX 4095
 
9.7%
NY 2953
 
7.0%
PA 2849
 
6.7%
FL 1920
 
4.5%
OH 1229
 
2.9%
UT 1166
 
2.8%
TN 1147
 
2.7%
WA 1050
 
2.5%
MN 1004
 
2.4%
Other values (41) 18001
42.5%

Length

2024-01-23T12:29:31.669586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 6893
 
16.3%
tx 4095
 
9.7%
ny 2953
 
7.0%
pa 2849
 
6.7%
fl 1920
 
4.5%
oh 1229
 
2.9%
ut 1166
 
2.8%
tn 1147
 
2.7%
wa 1050
 
2.5%
mn 1004
 
2.4%
Other values (41) 18001
42.5%

Most occurring characters

ValueCountFrequency (%)
A 14780
17.5%
C 8921
10.5%
N 8694
10.3%
T 7296
 
8.6%
M 5576
 
6.6%
I 4601
 
5.4%
O 4135
 
4.9%
X 4095
 
4.8%
Y 3796
 
4.5%
L 3394
 
4.0%
Other values (14) 19326
22.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 84614
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14780
17.5%
C 8921
10.5%
N 8694
10.3%
T 7296
 
8.6%
M 5576
 
6.6%
I 4601
 
5.4%
O 4135
 
4.9%
X 4095
 
4.8%
Y 3796
 
4.5%
L 3394
 
4.0%
Other values (14) 19326
22.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 84614
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14780
17.5%
C 8921
10.5%
N 8694
10.3%
T 7296
 
8.6%
M 5576
 
6.6%
I 4601
 
5.4%
O 4135
 
4.9%
X 4095
 
4.8%
Y 3796
 
4.5%
L 3394
 
4.0%
Other values (14) 19326
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14780
17.5%
C 8921
10.5%
N 8694
10.3%
T 7296
 
8.6%
M 5576
 
6.6%
I 4601
 
5.4%
O 4135
 
4.9%
X 4095
 
4.8%
Y 3796
 
4.5%
L 3394
 
4.0%
Other values (14) 19326
22.8%

BankState
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct51
Distinct (%)0.1%
Missing11
Missing (%)< 0.1%
Memory size43.9 KiB
CA
6476 
NC
3320 
IL
2944 
OH
2785 
RI
2541 
Other values (46)
24230 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters84592
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSD
2nd rowOK
3rd rowNJ
4th rowSD
5th rowCA

Common Values

ValueCountFrequency (%)
CA 6476
15.3%
NC 3320
 
7.8%
IL 2944
 
7.0%
OH 2785
 
6.6%
RI 2541
 
6.0%
TX 2457
 
5.8%
SD 2382
 
5.6%
NY 2197
 
5.2%
PA 1307
 
3.1%
UT 1133
 
2.7%
Other values (41) 14754
34.9%

Length

2024-01-23T12:29:31.709383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 6476
15.3%
nc 3320
 
7.8%
il 2944
 
7.0%
oh 2785
 
6.6%
ri 2541
 
6.0%
tx 2457
 
5.8%
sd 2382
 
5.6%
ny 2197
 
5.2%
pa 1307
 
3.1%
ut 1133
 
2.7%
Other values (41) 14754
34.9%

Most occurring characters

ValueCountFrequency (%)
A 11316
13.4%
C 10852
12.8%
N 8915
10.5%
I 7447
 
8.8%
T 5135
 
6.1%
O 5117
 
6.0%
L 4216
 
5.0%
M 4099
 
4.8%
D 3776
 
4.5%
H 3208
 
3.8%
Other values (14) 20511
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 84592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 11316
13.4%
C 10852
12.8%
N 8915
10.5%
I 7447
 
8.8%
T 5135
 
6.1%
O 5117
 
6.0%
L 4216
 
5.0%
M 4099
 
4.8%
D 3776
 
4.5%
H 3208
 
3.8%
Other values (14) 20511
24.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 84592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 11316
13.4%
C 10852
12.8%
N 8915
10.5%
I 7447
 
8.8%
T 5135
 
6.1%
O 5117
 
6.0%
L 4216
 
5.0%
M 4099
 
4.8%
D 3776
 
4.5%
H 3208
 
3.8%
Other values (14) 20511
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 11316
13.4%
C 10852
12.8%
N 8915
10.5%
I 7447
 
8.8%
T 5135
 
6.1%
O 5117
 
6.0%
L 4216
 
5.0%
M 4099
 
4.8%
D 3776
 
4.5%
H 3208
 
3.8%
Other values (14) 20511
24.2%

DisbursementGross
Categorical

HIGH CARDINALITY 

Distinct2694
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size168.3 KiB
$100,000.00
 
2773
$50,000.00
 
2016
$25,000.00
 
1433
$5,000.00
 
1298
$60,000.00
 
1041
Other values (2689)
33746 

Length

Max length14
Median length12
Mean length11.495237
Min length10

Characters and Unicode

Total characters486329
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique685 ?
Unique (%)1.6%

Sample

1st row$80,000.00
2nd row$287,000.00
3rd row$31,983.00
4th row$229,000.00
5th row$525,000.00

Common Values

ValueCountFrequency (%)
$100,000.00 2773
 
6.6%
$50,000.00 2016
 
4.8%
$25,000.00 1433
 
3.4%
$5,000.00 1298
 
3.1%
$60,000.00 1041
 
2.5%
$150,000.00 971
 
2.3%
$80,000.00 955
 
2.3%
$145,000.00 773
 
1.8%
$17,000.00 729
 
1.7%
$10,000.00 690
 
1.6%
Other values (2684) 29628
70.0%

Length

2024-01-23T12:29:31.755310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100,000.00 2773
 
6.6%
50,000.00 2016
 
4.8%
25,000.00 1433
 
3.4%
5,000.00 1298
 
3.1%
60,000.00 1041
 
2.5%
150,000.00 971
 
2.3%
80,000.00 955
 
2.3%
145,000.00 773
 
1.8%
17,000.00 729
 
1.7%
10,000.00 690
 
1.6%
Other values (2684) 29628
70.0%

Most occurring characters

ValueCountFrequency (%)
0 217564
44.7%
, 43223
 
8.9%
$ 42307
 
8.7%
. 42307
 
8.7%
42307
 
8.7%
5 20651
 
4.2%
1 18647
 
3.8%
2 13317
 
2.7%
3 9574
 
2.0%
4 8893
 
1.8%
Other values (4) 27539
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316185
65.0%
Other Punctuation 85530
 
17.6%
Currency Symbol 42307
 
8.7%
Space Separator 42307
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 217564
68.8%
5 20651
 
6.5%
1 18647
 
5.9%
2 13317
 
4.2%
3 9574
 
3.0%
4 8893
 
2.8%
7 7929
 
2.5%
6 7457
 
2.4%
8 6532
 
2.1%
9 5621
 
1.8%
Other Punctuation
ValueCountFrequency (%)
, 43223
50.5%
. 42307
49.5%
Currency Symbol
ValueCountFrequency (%)
$ 42307
100.0%
Space Separator
ValueCountFrequency (%)
42307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 486329
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 217564
44.7%
, 43223
 
8.9%
$ 42307
 
8.7%
. 42307
 
8.7%
42307
 
8.7%
5 20651
 
4.2%
1 18647
 
3.8%
2 13317
 
2.7%
3 9574
 
2.0%
4 8893
 
1.8%
Other values (4) 27539
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 486329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 217564
44.7%
, 43223
 
8.9%
$ 42307
 
8.7%
. 42307
 
8.7%
42307
 
8.7%
5 20651
 
4.2%
1 18647
 
3.8%
2 13317
 
2.7%
3 9574
 
2.0%
4 8893
 
1.8%
Other values (4) 27539
 
5.7%

GrAppv
Categorical

HIGH CARDINALITY 

Distinct1425
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size126.2 KiB
$100,000.00
3257 
$50,000.00
 
2934
$25,000.00
 
2153
$5,000.00
 
1426
$10,000.00
 
1330
Other values (1420)
31207 

Length

Max length14
Median length12
Mean length11.469662
Min length10

Characters and Unicode

Total characters485247
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique294 ?
Unique (%)0.7%

Sample

1st row$80,000.00
2nd row$287,000.00
3rd row$30,000.00
4th row$229,000.00
5th row$525,000.00

Common Values

ValueCountFrequency (%)
$100,000.00 3257
 
7.7%
$50,000.00 2934
 
6.9%
$25,000.00 2153
 
5.1%
$5,000.00 1426
 
3.4%
$10,000.00 1330
 
3.1%
$60,000.00 1088
 
2.6%
$150,000.00 1051
 
2.5%
$80,000.00 988
 
2.3%
$15,000.00 905
 
2.1%
$20,000.00 902
 
2.1%
Other values (1415) 26273
62.1%

Length

2024-01-23T12:29:31.805599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100,000.00 3257
 
7.7%
50,000.00 2934
 
6.9%
25,000.00 2153
 
5.1%
5,000.00 1426
 
3.4%
10,000.00 1330
 
3.1%
60,000.00 1088
 
2.6%
150,000.00 1051
 
2.5%
80,000.00 988
 
2.3%
15,000.00 905
 
2.1%
20,000.00 902
 
2.1%
Other values (1415) 26273
62.1%

Most occurring characters

ValueCountFrequency (%)
0 234078
48.2%
, 43234
 
8.9%
$ 42307
 
8.7%
. 42307
 
8.7%
42307
 
8.7%
5 20609
 
4.2%
1 16194
 
3.3%
2 11923
 
2.5%
3 7367
 
1.5%
4 6360
 
1.3%
Other values (4) 18561
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315092
64.9%
Other Punctuation 85541
 
17.6%
Currency Symbol 42307
 
8.7%
Space Separator 42307
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234078
74.3%
5 20609
 
6.5%
1 16194
 
5.1%
2 11923
 
3.8%
3 7367
 
2.3%
4 6360
 
2.0%
7 5694
 
1.8%
6 5310
 
1.7%
8 4480
 
1.4%
9 3077
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 43234
50.5%
. 42307
49.5%
Currency Symbol
ValueCountFrequency (%)
$ 42307
100.0%
Space Separator
ValueCountFrequency (%)
42307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 485247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 234078
48.2%
, 43234
 
8.9%
$ 42307
 
8.7%
. 42307
 
8.7%
42307
 
8.7%
5 20609
 
4.2%
1 16194
 
3.3%
2 11923
 
2.5%
3 7367
 
1.5%
4 6360
 
1.3%
Other values (4) 18561
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 485247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 234078
48.2%
, 43234
 
8.9%
$ 42307
 
8.7%
. 42307
 
8.7%
42307
 
8.7%
5 20609
 
4.2%
1 16194
 
3.3%
2 11923
 
2.5%
3 7367
 
1.5%
4 6360
 
1.3%
Other values (4) 18561
 
3.8%

SBA_Appv
Categorical

HIGH CARDINALITY 

Distinct2005
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size163.0 KiB
$25,000.00
 
2382
$12,500.00
 
1705
$90,000.00
 
1023
$4,250.00
 
985
$50,000.00
 
982
Other values (2000)
35230 

Length

Max length14
Median length11
Mean length11.284374
Min length10

Characters and Unicode

Total characters477408
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique443 ?
Unique (%)1.0%

Sample

1st row$68,000.00
2nd row$229,600.00
3rd row$15,000.00
4th row$229,000.00
5th row$393,750.00

Common Values

ValueCountFrequency (%)
$25,000.00 2382
 
5.6%
$12,500.00 1705
 
4.0%
$90,000.00 1023
 
2.4%
$4,250.00 985
 
2.3%
$50,000.00 982
 
2.3%
$5,000.00 957
 
2.3%
$116,000.00 757
 
1.8%
$51,000.00 742
 
1.8%
$80,000.00 729
 
1.7%
$13,600.00 715
 
1.7%
Other values (1995) 31330
74.1%

Length

2024-01-23T12:29:31.855869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25,000.00 2382
 
5.6%
12,500.00 1705
 
4.0%
90,000.00 1023
 
2.4%
4,250.00 985
 
2.3%
50,000.00 982
 
2.3%
5,000.00 957
 
2.3%
116,000.00 757
 
1.8%
51,000.00 742
 
1.8%
80,000.00 729
 
1.7%
13,600.00 715
 
1.7%
Other values (1995) 31330
74.1%

Most occurring characters

ValueCountFrequency (%)
0 192470
40.3%
, 42736
 
9.0%
$ 42307
 
8.9%
. 42307
 
8.9%
42307
 
8.9%
5 28769
 
6.0%
2 18959
 
4.0%
1 18682
 
3.9%
3 9861
 
2.1%
7 9728
 
2.0%
Other values (4) 29282
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 307751
64.5%
Other Punctuation 85043
 
17.8%
Currency Symbol 42307
 
8.9%
Space Separator 42307
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 192470
62.5%
5 28769
 
9.3%
2 18959
 
6.2%
1 18682
 
6.1%
3 9861
 
3.2%
7 9728
 
3.2%
6 8048
 
2.6%
4 7649
 
2.5%
8 7052
 
2.3%
9 6533
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 42736
50.3%
. 42307
49.7%
Currency Symbol
ValueCountFrequency (%)
$ 42307
100.0%
Space Separator
ValueCountFrequency (%)
42307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 477408
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 192470
40.3%
, 42736
 
9.0%
$ 42307
 
8.9%
. 42307
 
8.9%
42307
 
8.9%
5 28769
 
6.0%
2 18959
 
4.0%
1 18682
 
3.9%
3 9861
 
2.1%
7 9728
 
2.0%
Other values (4) 29282
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 477408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 192470
40.3%
, 42736
 
9.0%
$ 42307
 
8.9%
. 42307
 
8.9%
42307
 
8.9%
5 28769
 
6.0%
2 18959
 
4.0%
1 18682
 
3.9%
3 9861
 
2.1%
7 9728
 
2.0%
Other values (4) 29282
 
6.1%

UrbanRural
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size330.7 KiB
0
24037 
1
11759 
2
6511 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42307
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Length

2024-01-23T12:29:31.900001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-23T12:29:31.938267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Most occurring characters

ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42307
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 42307
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Interactions

2024-01-23T12:29:29.569497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.035405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.433961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.769439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.106341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.459724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.823896image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.232186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.609335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.087251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.472697image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.808855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.145229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.498842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.863472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.272133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.652170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.146154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.513209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.849302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.194283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.542127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.903705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.314141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.696274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.206656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.556881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.892417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.238039image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.590589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.946371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.358777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.739036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.267239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.599762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.934669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.282718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.634441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.987450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.400673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.782784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.311214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.643447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.979061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.330145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.683380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.026919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.443278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.821508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.347742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.681302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.017582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.369495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.722733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.148499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.482082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.866496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.391259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:27.725601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.062717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.414972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:28.768069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.189359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-01-23T12:29:29.525428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2024-01-23T12:29:31.973850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ApprovalFYBankStateCreateJobFranchiseCodeLowDocMIS_StatusNewExistNoEmpRetainedJobRevLineCrSectorStateTermUnnamed: 0UrbanRural
ApprovalFY1.0000.079-0.0410.0760.0650.0420.112-0.079-0.0250.062-0.0590.073-0.055-0.0050.041
BankState0.0791.000-0.0070.0450.2140.1250.2430.021-0.0180.195-0.0360.7180.0360.0060.205
CreateJob-0.041-0.0071.000-0.2490.0570.0270.0750.0130.3790.1480.2140.062-0.029-0.0030.072
FranchiseCode0.0760.045-0.2491.0000.1910.0820.111-0.109-0.2720.071-0.2410.048-0.039-0.0010.074
LowDoc0.0650.2140.0570.1911.0000.2250.429-0.178-0.1360.153-0.0360.1990.0060.0020.237
MIS_Status0.0420.1250.0270.0820.2251.0000.1050.172-0.0580.237-0.0440.1030.119-0.0020.195
NewExist0.1120.2430.0750.1110.4290.1051.000-0.215-0.1350.117-0.0200.224-0.072-0.0120.220
NoEmp-0.0790.0210.013-0.109-0.1780.172-0.2151.000-0.0340.0670.0120.0520.258-0.0010.088
RetainedJob-0.025-0.0180.379-0.272-0.136-0.058-0.135-0.0341.0000.0300.2590.047-0.0900.0020.028
RevLineCr0.0620.1950.1480.0710.1530.2370.1170.0670.0301.0000.0460.195-0.142-0.0010.269
Sector-0.059-0.0360.214-0.241-0.036-0.044-0.0200.0120.2590.0461.0000.114-0.0110.0030.296
State0.0730.7180.0620.0480.1990.1030.2240.0520.0470.1950.1141.0000.0200.0020.196
Term-0.0550.036-0.029-0.0390.0060.119-0.0720.258-0.090-0.142-0.0110.0201.000-0.0040.211
Unnamed: 0-0.0050.006-0.003-0.0010.002-0.002-0.012-0.0010.002-0.0010.0030.002-0.0041.0000.009
UrbanRural0.0410.2050.0720.0740.2370.1950.2200.0880.0280.2690.2960.1960.2110.0091.000

Missing values

2024-01-23T12:29:29.935082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-23T12:29:30.065032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-23T12:29:30.209821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0TermNoEmpNewExistCreateJobRetainedJobFranchiseCodeRevLineCrLowDocDisbursementDateMIS_StatusSectorApprovalDateApprovalFYCityStateBankStateDisbursementGrossGrAppvSBA_AppvUrbanRural
00163211.0001NN31-Jan-981022-Sep-062006PHOENIXAZSD$80,000.00$80,000.00$68,000.000
118461.04000N31-Oct-9316230-Jun-921992MCALESTEROKOK$287,000.00$287,000.00$229,600.000
22242451.04900NN31-Aug-0114218-Apr-012001HAWTHORNENJNJ$31,983.00$30,000.00$15,000.001
3323741.0000NN31-Aug-071336-Oct-032004NASHVILLETNSD$229,000.00$229,000.00$229,000.000
4418401.0000NN8-Jun-831017-Dec-992000POMONACACA$525,000.00$525,000.00$393,750.000
556071.04100YN1-Apr-1204426-Nov-931994APLINGTONIAIA$69,991.00$70,000.00$35,000.000
663901.015100N8-Nov-111234-Jan-052005DALLASTXCA$50,000.00$50,000.00$25,000.000
778252.0001NC31-Jan-951021-Nov-012002HUDSONNHNH$414,000.00$414,000.00$414,000.000
885762.0000NC31-Jan-9516111-Jan-951995WILLISTONNDND$112,500.00$112,500.00$101,250.000
992511.0001NN30-Apr-071023-Mar-042004MESAAZAZ$50,000.00$50,000.00$25,000.002
Unnamed: 0TermNoEmpNewExistCreateJobRetainedJobFranchiseCodeRevLineCrLowDocDisbursementDateMIS_StatusSectorApprovalDateApprovalFYCityStateBankStateDisbursementGrossGrAppvSBA_AppvUrbanRural
42297422975721.000960NN29-Jun-01108-Mar-961996SACRAMENTOCACO$105,000.00$105,000.00$89,250.000
422984229882151.0110725900N30-Apr-0614415-May-032003DALLASTXTX$50,000.00$50,000.00$25,000.000
42299422991021.0010YN30-Jun-080728-Mar-002000CLARENCENYNY$670,000.00$670,000.00$670,000.001
42300423008231.0001NN31-Dec-051429-Jul-032003NEW YORKNYIL$150,000.00$150,000.00$135,000.000
42301423018201.01811YN10-Dec-961237-Jul-092009PITTSBURGHPAPA$5,000.00$5,000.00$4,250.001
4230242302283141.0001NN31-Jan-98102-Mar-951995PHILADELPHIAPAPA$80,000.00$80,000.00$68,000.000
42303423035321.0000YN3-Apr-911426-Jun-072007LOS ANGELESCASD$5,000.00$5,000.00$4,250.001
42304423045962.0001NN28-Feb-0314214-Mar-032003COLUMBUSOHOH$60,000.00$60,000.00$51,000.000
4230542305295181.0080NN10-Dec-9714223-Aug-891989CLOQUETMNMN$294,000.00$294,000.00$220,500.000
42306423068441.0080NN31-Oct-8917212-Apr-112011SAN GABRIELCANC$67,500.00$67,500.00$50,625.000